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All Journal Indonesian Council of Premier Statistical Science
Hasibuan, Muhammad Angga Piansyah
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Computer Science & IT Economics, Econometrics & Finance Mathematics Public Health Social Sciences

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Analysis of the 2022 Shallot Price Grouping in the Areas of Arengka, Cikpuan, Incense, and Suka Ramai Using the Hierarchical Clustering Method Akbar, Muhammad Rizki; Hasibuan, Muhammad Angga Piansyah; Prasetyo, Restu
Indonesian Council of Premier Statistical Science Vol 4, No 2 (2025): August 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/icopss.v4i2.37870

Abstract

This study aims to analyze the price grouping of shallots in four regions, namely Arengka, Cikpuan, Incense, and Suka Ramai in 2022 using the Hierarchical Clustering method. The data used is secondary data on the price of shallots from each region. The analysis was carried out with a multivariate statistical approach to identify price patterns and form clusters based on price similarities between regions. The results of the study show that the price of shallots in the four regions can be grouped into two main clusters. The first cluster consists of areas with lower shallot prices (<66.67 rupiah), while the second cluster consists of areas with higher prices (>66.67 rupiah). The distance between the clusters of 5.22715 shows a significant difference in price characteristics. These findings provide strategic benefits for industry players and policy makers in optimizing distribution and developing marketing strategies that are more targeted in each region. Overall, the Hierarchical Clustering method has proven to be effective in identifying shallot price patterns and can be used as a basis for data-driven decision-making in the agricultural sector
Co-Authors Muhammad Rizki Akbar, Muhammad Rizki Prasetyo, Restu